Machine Learning and Tracking Terrorists
As the accused accomplices of terrorist Mohammed Merah stood trial in France in October 2017, aggregate web page traffic related to the location of the trial, hostile vehicle mitigation equipment, and eyewitness videos of past vehicle attacks in France all surged. A few days later, police announced they had arrested two men for trespassing on the judicial complex where the trial was taking place, one of whom, according to press reports, had loose ties to a terrorist cell in Paris.
Was this activity simply coincidence or the open web traces of pre-attack planning? When I worked on counterterrorism programs in the U.S. Government, I often faced this type of challenge, separating credible threat intelligence from spurious. With the proliferation of indicators and warnings derived from publicly available information, the challenge is now even greater.
Recent technological advances can help address this challenge. Knowing that internet activity precedes real world action, we can use machine learning to find predictive trends in anonymized and aggregated raw data.